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Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface.

David Perpetuini1,2, Mehmet Günal3, Nicole Chiou4

  • 1Department of Neuroscience, Imaging and Clinical Sciences, G. D'Annunzio University of Chieti-Pescara, 66100 Chieti, Italy.

Bioengineering (Basel, Switzerland)
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Summary

This study demonstrates a novel brain-computer interface (BCI) using near-infrared (NIR) imaging to detect visual stimuli. Machine learning improved the classification of visual field quadrants, advancing BCI applications.

Keywords:
brain–computer interface (BCI)event-related optical signals (EROS)fast optical signals (FOS)machine learning (ML)retinotopy

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Optical Imaging

Background:

  • Brain-computer interfaces (BCIs) enable device control via brain activity.
  • Near-infrared (NIR) imaging offers portable neuroimaging for BCIs.
  • Fast optical signals (FOS) from NIR imaging provide good spatiotemporal resolution but suffer from low signal-to-noise ratios, hindering BCI use.

Purpose of the Study:

  • To investigate the application of fast optical signals (FOS) from NIR imaging in a brain-computer interface (BCI).
  • To develop a machine learning approach for fast estimation of visual-field quadrant stimulation using FOS.
  • To assess the feasibility of generalizable retinotopy classification for real-time BCI applications.

Main Methods:

  • Acquired FOS using a frequency-domain optical system at 690 nm and 830 nm during visual stimulation.
  • Utilized photon count (Direct Current, DC) and time of flight (phase) measures.
  • Employed a cross-validated support vector machine classifier with wavelet coherence features for classification.

Main Results:

  • Achieved above-chance performance in differentiating visual stimulation quadrants (left vs. right, top vs. bottom).
  • Obtained a best classification accuracy of ~63% for superior and inferior quadrants using DC at 830 nm.
  • Reported an information transfer rate of ~6 bits/min.

Conclusions:

  • This study presents the first attempt at generalizable retinotopy classification using FOS.
  • The developed method shows promise for enhancing the application of FOS in real-time BCIs.
  • The findings pave the way for improved BCI systems leveraging optical neuroimaging techniques.